Please use this identifier to cite or link to this item: https://doi.org/10.1186/s13059-020-01968-7
Title: NanoVar: Accurate characterization of patients' genomic structural variants using low-depth nanopore sequencing
Authors: Tham, C.Y. 
Tirado-Magallanes, R. 
Goh, Y. 
Fullwood, M.J. 
Koh, B.T.H.
Wang, W. 
Ng, C.H. 
Chng, W.J. 
Thiery, A. 
Tenen, D.G. 
Benoukraf, T. 
Keywords: Long reads
Low depth
Oxford Nanopore sequencing
Structural variants
SV characterization
Third-generation sequencing
WGS
Issue Date: 3-Mar-2020
Publisher: BioMed Central Ltd.
Citation: Tham, C.Y., Tirado-Magallanes, R., Goh, Y., Fullwood, M.J., Koh, B.T.H., Wang, W., Ng, C.H., Chng, W.J., Thiery, A., Tenen, D.G., Benoukraf, T. (2020-03-03). NanoVar: Accurate characterization of patients' genomic structural variants using low-depth nanopore sequencing. Genome Biology 21 (1) : 56. ScholarBank@NUS Repository. https://doi.org/10.1186/s13059-020-01968-7
Rights: Attribution 4.0 International
Abstract: The recent advent of third-generation sequencing technologies brings promise for better characterization of genomic structural variants by virtue of having longer reads. However, long-read applications are still constrained by their high sequencing error rates and low sequencing throughput. Here, we present NanoVar, an optimized structural variant caller utilizing low-depth (8X) whole-genome sequencing data generated by Oxford Nanopore Technologies. NanoVar exhibits higher structural variant calling accuracy when benchmarked against current tools using low-depth simulated datasets. In patient samples, we successfully validate structural variants characterized by NanoVar and uncover normal alternative sequences or alleles which are present in healthy individuals. © 2020 The Author(s).
Source Title: Genome Biology
URI: https://scholarbank.nus.edu.sg/handle/10635/198614
ISSN: 14747596
DOI: 10.1186/s13059-020-01968-7
Rights: Attribution 4.0 International
Appears in Collections:Staff Publications
Elements

Show full item record
Files in This Item:
File Description SizeFormatAccess SettingsVersion 
10_1186_s13059_020_01968_7.pdf2.27 MBAdobe PDF

OPEN

NoneView/Download

SCOPUSTM   
Citations

16
checked on Dec 6, 2021

Page view(s)

26
checked on Dec 2, 2021

Google ScholarTM

Check

Altmetric


This item is licensed under a Creative Commons License Creative Commons